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Learning taxonomies by dependence maximization

Blaschko, MB; Gretton, A; (2009) Learning taxonomies by dependence maximization. In: (pp. pp. 153-160).

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We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, taking into account the relations between different clusters is shown to substantially improve the quality of the clustering, when compared with state-ofthe- art algorithms in the literature (both spectral clustering and a previous dependence maximization approach). We demonstrate our algorithm on image and text data.

Type: Proceedings paper
Title: Learning taxonomies by dependence maximization
ISBN-13: 9781605609492
URI: http://discovery.ucl.ac.uk/id/eprint/1334314
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